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arXiv cs.LG
arXiv cs.LG
7/7/2026
A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

Short summary

Researchers developed a granularity-aware EEG feature framework organizing brain signals at global, regional, and channel-specific scales to predict four psychopathology dimensions in young people using tree-based machine learning. Tested on the Healthy Brain Network cohort, the approach detected weak but statistically consistent signals related to mental health phenotypes. Although effect sizes were modest, dimension-specific spatial and spectral patterns aligned with known neuroscience, supporting EEG-based approaches for future clinical phenotyping studies.

  • •EEG framework organizes multi-scale brain signals to predict mental health dimensions
  • •Tree-based models detected weak but consistent signals in pediatric psychopathology data
  • •Results support EEG as a complementary clinical tool despite modest effect sizes

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Read full article at arXiv cs.LG

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